Data Integration for Classification Problems Employing Gaussian Process Priors

Part of Advances in Neural Information Processing Systems 19 (NIPS 2006)

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Mark Girolami, Mingjun Zhong


By adopting Gaussian process priors a fully Bayesian solution to the problem of integrating possibly heterogeneous data sets within a classification setting is presented. Approximate inference schemes employing Variational & Expectation Propagation based methods are developed and rigorously assessed. We demonstrate our approach to integrating multiple data sets on a large scale protein fold prediction problem where we infer the optimal combinations of covariance functions and achieve state-of-the-art performance without resorting to any ad hoc parameter tuning and classifier combination.